Fingerprints (i.e., friction ridge signatures) illustrate a pattern of friction ridges and valleys having features that may be identified. In two-dimensional fingerprint representations associated with existing fingerprint imaging technology, the top of the friction ridges appear to be flat and/or planar. However, friction ridges that make up the friction ridge pattern reflected in a fingerprint are more like mountain ranges, undulating up and down with areas of lower elevation and areas where peaks exist. These variations are not visible in typical fingerprints because the subject's finger is pressed against an imaging surface causing the peaks and undulating areas to flatten such that they are captured as if they were flat. As such, these variations are ignored and are not visible as identification features.
Instead, conventional fingerprint classification and identification methods focus on the type and location of level I and level II features. Level I features, such as loops, arches, tents, deltas, and whorls, are mainly used to classify or subdivide fingerprints into broad categories, but do not provide sufficient discriminating power to identify individuals.
Level II features, such as ridge endings, bifurcations, and dots provide the basis of present day fingerprint identification algorithms. These features are classifications of the continuity of fingerprint ridge lines. To automate this process, the contrast of fingerprint images is often increased so that image processing algorithms can more accurately follow ridge lines to locate deviations from ridge line continuity. Relational maps of level II features are compared to national databases in order to identify individuals by their fingerprints.
Additional Level III features broadly arise from fine details of fingerprint patterns, ridges and valleys. Typical level III details may include ridge shape, width, and path deviation, pores, incipient ridges, breaks, creases, scars, and/or a variety of ridge edge contour details. Human experts may use Level III features to confirm the identity of an individual after a preliminary match based on level II features. Unfortunately, some level III features show significant variability within the same individual from fingerprints taken under different conditions. These factors have so far raised significant challenges for the use of level III features in automated fingerprint recognition algorithms.